mirror of
https://github.com/amazon-science/chronos-forecasting
synced 2026-05-23 09:39:35 +00:00
e48f48071f
*Issue #, if available:* *Description of changes:* This PR adds `predict_df` to the base pipeline which enables pandas support for the univariate Chronos and Chronos-Bolt models. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
445 lines
15 KiB
Python
445 lines
15 KiB
Python
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import pytest
|
|
import torch
|
|
|
|
from chronos import (
|
|
BaseChronosPipeline,
|
|
ChronosConfig,
|
|
ChronosPipeline,
|
|
MeanScaleUniformBins,
|
|
)
|
|
from test.util import create_df, get_forecast_start_times, validate_tensor
|
|
|
|
DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos-model"
|
|
|
|
|
|
@pytest.fixture
|
|
def pipeline() -> ChronosPipeline:
|
|
return BaseChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu")
|
|
|
|
|
|
def test_base_chronos_pipeline_loads_from_huggingface():
|
|
BaseChronosPipeline.from_pretrained("amazon/chronos-t5-tiny", device_map="cpu")
|
|
|
|
|
|
@pytest.mark.parametrize("n_numerical_tokens", [5, 10, 27])
|
|
@pytest.mark.parametrize("n_special_tokens", [2, 5, 13])
|
|
def test_tokenizer_consistency(n_numerical_tokens: int, n_special_tokens: int):
|
|
n_tokens = n_numerical_tokens + n_special_tokens
|
|
|
|
config = ChronosConfig(
|
|
tokenizer_class="MeanScaleUniformBins",
|
|
tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
|
|
n_tokens=n_tokens,
|
|
n_special_tokens=n_special_tokens,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
use_eos_token=True,
|
|
model_type="seq2seq",
|
|
context_length=512,
|
|
prediction_length=64,
|
|
num_samples=20,
|
|
temperature=1.0,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
)
|
|
|
|
tokenizer = config.create_tokenizer()
|
|
assert isinstance(tokenizer, MeanScaleUniformBins)
|
|
|
|
context = tokenizer.centers.unsqueeze(0) # add batch dimension
|
|
scale = torch.ones((1,)) # fix the scale to one to turn off scaling
|
|
|
|
token_ids, _, _ = tokenizer._input_transform(context, scale=scale)
|
|
|
|
samples = tokenizer.output_transform(
|
|
token_ids.unsqueeze(1), # add sample dimension
|
|
scale=scale,
|
|
)
|
|
|
|
assert (samples[0, 0, :] == context).all()
|
|
|
|
|
|
@pytest.mark.xfail
|
|
@pytest.mark.parametrize("n_numerical_tokens", [5, 10, 27])
|
|
@pytest.mark.parametrize("n_special_tokens", [2, 5, 13])
|
|
@pytest.mark.parametrize("use_eos_token", [False, True])
|
|
def test_tokenizer_fixed_data(n_numerical_tokens: int, n_special_tokens: int, use_eos_token: bool):
|
|
n_tokens = n_numerical_tokens + n_special_tokens
|
|
context_length = 3
|
|
|
|
config = ChronosConfig(
|
|
tokenizer_class="MeanScaleUniformBins",
|
|
tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
|
|
n_tokens=n_tokens,
|
|
n_special_tokens=n_special_tokens,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
use_eos_token=use_eos_token,
|
|
model_type="seq2seq",
|
|
context_length=512,
|
|
prediction_length=64,
|
|
num_samples=20,
|
|
temperature=1.0,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
)
|
|
|
|
tokenizer = config.create_tokenizer()
|
|
|
|
context = torch.tensor(
|
|
[
|
|
[-3.7, 3.7],
|
|
[-42.0, 42.0],
|
|
]
|
|
)
|
|
batch_size, _ = context.shape
|
|
|
|
token_ids, attention_mask, scale = tokenizer.context_input_transform(context)
|
|
|
|
assert token_ids.shape == (batch_size, context_length + 1 * use_eos_token)
|
|
assert all(token_ids[:, 0] == torch.tensor([0]).repeat(batch_size))
|
|
assert all(token_ids[:, 1] == torch.tensor([n_special_tokens]).repeat(batch_size))
|
|
assert all(token_ids[:, 2] == torch.tensor([n_tokens - 1]).repeat(batch_size))
|
|
|
|
if use_eos_token:
|
|
assert all(token_ids[:, 3] == torch.tensor([1]).repeat(batch_size))
|
|
|
|
samples = tokenizer.output_transform(
|
|
torch.arange(n_special_tokens, n_tokens).unsqueeze(0).repeat(batch_size, 1, 1),
|
|
tokenizer_state=scale,
|
|
)
|
|
|
|
assert (samples[:, 0, [0, -1]] == context).all()
|
|
|
|
|
|
@pytest.mark.xfail
|
|
@pytest.mark.parametrize("use_eos_token", [False, True])
|
|
def test_tokenizer_random_data(use_eos_token: bool):
|
|
context_length = 8
|
|
n_tokens = 256
|
|
n_special_tokens = 2
|
|
|
|
config = ChronosConfig(
|
|
tokenizer_class="MeanScaleUniformBins",
|
|
tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
|
|
n_tokens=n_tokens,
|
|
n_special_tokens=n_special_tokens,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
use_eos_token=use_eos_token,
|
|
model_type="seq2seq",
|
|
context_length=context_length,
|
|
prediction_length=64,
|
|
num_samples=20,
|
|
temperature=1.0,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
)
|
|
|
|
tokenizer = config.create_tokenizer()
|
|
|
|
context = torch.tensor(
|
|
[
|
|
[torch.nan, torch.nan, 1.0, 1.1, torch.nan, 2.0],
|
|
[3.0, torch.nan, 3.9, 4.0, 4.1, 4.9],
|
|
]
|
|
)
|
|
|
|
token_ids, attention_mask, scale = tokenizer.context_input_transform(context)
|
|
|
|
assert token_ids.shape == (
|
|
*context.shape[:-1],
|
|
context_length + 1 * use_eos_token,
|
|
)
|
|
assert attention_mask.shape == (
|
|
*context.shape[:-1],
|
|
context_length + 1 * use_eos_token,
|
|
)
|
|
assert scale.shape == context.shape[:1]
|
|
|
|
sample_ids = torch.randint(low=n_special_tokens, high=n_tokens, size=(2, 10, 4))
|
|
sample_ids[0, 0, 0] = n_special_tokens
|
|
sample_ids[-1, -1, -1] = n_tokens - 1
|
|
|
|
samples = tokenizer.output_transform(sample_ids, scale)
|
|
|
|
assert samples.shape == (2, 10, 4)
|
|
|
|
|
|
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
|
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
|
def test_pipeline_predict(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
|
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
|
context = 10 * torch.rand(size=(4, 16)) + 10
|
|
context = context.to(dtype=input_dtype)
|
|
|
|
# input: tensor of shape (batch_size, context_length)
|
|
|
|
samples = pipeline.predict(context, num_samples=12, prediction_length=3)
|
|
validate_tensor(samples, shape=(4, 12, 3), dtype=torch.float32)
|
|
|
|
with pytest.raises(ValueError):
|
|
samples = pipeline.predict(context, num_samples=7, prediction_length=65, limit_prediction_length=True)
|
|
|
|
samples = pipeline.predict(context, num_samples=7, prediction_length=65, limit_prediction_length=False)
|
|
validate_tensor(samples, shape=(4, 7, 65), dtype=torch.float32)
|
|
|
|
# input: batch_size-long list of tensors of shape (context_length,)
|
|
|
|
samples = pipeline.predict(list(context), num_samples=12, prediction_length=3)
|
|
validate_tensor(samples, shape=(4, 12, 3), dtype=torch.float32)
|
|
|
|
with pytest.raises(ValueError):
|
|
samples = pipeline.predict(
|
|
list(context),
|
|
num_samples=7,
|
|
prediction_length=65,
|
|
limit_prediction_length=True,
|
|
)
|
|
|
|
samples = pipeline.predict(
|
|
list(context),
|
|
num_samples=7,
|
|
prediction_length=65,
|
|
limit_prediction_length=False,
|
|
)
|
|
validate_tensor(samples, shape=(4, 7, 65), dtype=torch.float32)
|
|
|
|
# input: tensor of shape (context_length,)
|
|
|
|
samples = pipeline.predict(context[0, ...], num_samples=12, prediction_length=3)
|
|
validate_tensor(samples, shape=(1, 12, 3), dtype=torch.float32)
|
|
|
|
with pytest.raises(ValueError):
|
|
samples = pipeline.predict(
|
|
context[0, ...],
|
|
num_samples=7,
|
|
prediction_length=65,
|
|
limit_prediction_length=True,
|
|
)
|
|
|
|
samples = pipeline.predict(
|
|
context[0, ...],
|
|
num_samples=7,
|
|
prediction_length=65,
|
|
)
|
|
validate_tensor(samples, shape=(1, 7, 65), dtype=torch.float32)
|
|
|
|
|
|
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
|
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
|
@pytest.mark.parametrize("prediction_length", [3, 65])
|
|
@pytest.mark.parametrize("quantile_levels", [[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [0.1, 0.5, 0.9]])
|
|
def test_pipeline_predict_quantiles(
|
|
model_dtype: torch.dtype,
|
|
input_dtype: torch.dtype,
|
|
prediction_length: int,
|
|
quantile_levels: list[int],
|
|
):
|
|
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
|
context = 10 * torch.rand(size=(4, 16)) + 10
|
|
context = context.to(dtype=input_dtype)
|
|
|
|
num_expected_quantiles = len(quantile_levels)
|
|
# input: tensor of shape (batch_size, context_length)
|
|
|
|
quantiles, mean = pipeline.predict_quantiles(
|
|
context,
|
|
num_samples=12,
|
|
prediction_length=prediction_length,
|
|
quantile_levels=quantile_levels,
|
|
)
|
|
validate_tensor(quantiles, (4, prediction_length, num_expected_quantiles), dtype=torch.float32)
|
|
validate_tensor(mean, (4, prediction_length), dtype=torch.float32)
|
|
|
|
# input: batch_size-long list of tensors of shape (context_length,)
|
|
|
|
quantiles, mean = pipeline.predict_quantiles(
|
|
list(context),
|
|
num_samples=12,
|
|
prediction_length=prediction_length,
|
|
quantile_levels=quantile_levels,
|
|
)
|
|
validate_tensor(quantiles, (4, prediction_length, num_expected_quantiles), dtype=torch.float32)
|
|
validate_tensor(mean, (4, prediction_length), dtype=torch.float32)
|
|
|
|
# input: tensor of shape (context_length,)
|
|
|
|
quantiles, mean = pipeline.predict_quantiles(
|
|
context[0, ...],
|
|
num_samples=12,
|
|
prediction_length=prediction_length,
|
|
quantile_levels=quantile_levels,
|
|
)
|
|
validate_tensor(quantiles, (1, prediction_length, num_expected_quantiles), dtype=torch.float32)
|
|
validate_tensor(mean, (1, prediction_length), dtype=torch.float32)
|
|
|
|
|
|
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
|
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
|
def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
|
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
|
d_model = pipeline.model.model.config.d_model
|
|
context = 10 * torch.rand(size=(4, 16)) + 10
|
|
context = context.to(dtype=input_dtype)
|
|
expected_embed_length = 16 + (1 if pipeline.model.config.use_eos_token else 0)
|
|
|
|
# input: tensor of shape (batch_size, context_length)
|
|
|
|
embedding, scale = pipeline.embed(context)
|
|
validate_tensor(embedding, shape=(4, expected_embed_length, d_model), dtype=model_dtype)
|
|
validate_tensor(scale, shape=(4,), dtype=torch.float32)
|
|
|
|
# input: batch_size-long list of tensors of shape (context_length,)
|
|
|
|
embedding, scale = pipeline.embed(list(context))
|
|
validate_tensor(embedding, shape=(4, expected_embed_length, d_model), dtype=model_dtype)
|
|
validate_tensor(scale, shape=(4,), dtype=torch.float32)
|
|
|
|
# input: tensor of shape (context_length,)
|
|
embedding, scale = pipeline.embed(context[0, ...])
|
|
validate_tensor(embedding, shape=(1, expected_embed_length, d_model), dtype=model_dtype)
|
|
validate_tensor(scale, shape=(1,), dtype=torch.float32)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"context_setup, expected_rows",
|
|
[
|
|
# Targets only
|
|
({}, 6), # 2 series * 3 predictions
|
|
# Different context lengths
|
|
(
|
|
{"series_ids": ["X", "Y", "Z"], "n_points": [10, 17, 56], "target_cols": ["custom_target"]},
|
|
9,
|
|
), # 3 series * 3 predictions
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
|
def test_predict_df_works_for_valid_inputs(pipeline, context_setup, expected_rows, freq):
|
|
prediction_length = 3
|
|
df = create_df(**context_setup, freq=freq)
|
|
forecast_start_times = get_forecast_start_times(df, freq)
|
|
|
|
series_ids = context_setup.get("series_ids", ["A", "B"])
|
|
target_columns = context_setup.get("target_cols", ["target"])
|
|
n_series = len(series_ids)
|
|
n_targets = len(target_columns)
|
|
result = pipeline.predict_df(df, target=target_columns[0], prediction_length=prediction_length)
|
|
|
|
assert len(result) == expected_rows
|
|
assert "item_id" in result.columns and np.all(
|
|
result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
|
|
)
|
|
assert "target_name" in result.columns and np.all(
|
|
result["target_name"].to_numpy() == np.tile(np.array(target_columns).repeat(prediction_length), n_series)
|
|
)
|
|
assert "timestamp" in result.columns and np.all(
|
|
result.groupby("item_id")["timestamp"].min().to_numpy() == pd.to_datetime(forecast_start_times).to_numpy()
|
|
)
|
|
assert "predictions" in result.columns
|
|
assert all(str(q) in result.columns for q in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
|
|
|
|
|
|
def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
|
|
df = create_df()
|
|
# Make timestamps non-uniform for series A
|
|
df.loc[df["item_id"] == "A", "timestamp"] = [
|
|
"2023-01-01",
|
|
"2023-01-02",
|
|
"2023-01-04",
|
|
"2023-01-05",
|
|
"2023-01-06",
|
|
"2023-01-07",
|
|
"2023-01-08",
|
|
"2023-01-09",
|
|
"2023-01-10",
|
|
"2023-01-11",
|
|
]
|
|
|
|
with pytest.raises(ValueError, match="not infer frequency"):
|
|
pipeline.predict_df(df)
|
|
|
|
|
|
def test_predict_df_with_inconsistent_frequencies_raises_error(pipeline):
|
|
df = pd.DataFrame(
|
|
{
|
|
"item_id": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"],
|
|
"timestamp": [
|
|
"2023-01-01",
|
|
"2023-01-02",
|
|
"2023-01-03",
|
|
"2023-01-04",
|
|
"2023-01-05",
|
|
"2023-01-01",
|
|
"2023-02-01",
|
|
"2023-03-01",
|
|
"2023-04-01",
|
|
"2023-05-01",
|
|
],
|
|
"target": [1.0] * 10,
|
|
}
|
|
)
|
|
|
|
with pytest.raises(ValueError, match="same frequency"):
|
|
pipeline.predict_df(df)
|
|
|
|
|
|
@pytest.mark.parametrize("n_tokens", [10, 1000, 10000])
|
|
def test_tokenizer_number_of_buckets(n_tokens):
|
|
config = ChronosConfig(
|
|
tokenizer_class="MeanScaleUniformBins",
|
|
tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
|
|
n_tokens=n_tokens,
|
|
n_special_tokens=2,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
use_eos_token=True,
|
|
model_type="seq2seq",
|
|
context_length=512,
|
|
prediction_length=64,
|
|
num_samples=20,
|
|
temperature=1.0,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
)
|
|
tokenizer = config.create_tokenizer()
|
|
|
|
n_numerical_tokens = config.n_tokens - config.n_special_tokens
|
|
|
|
# The tokenizer has one bucket too many as a result of an early bug. In order to
|
|
# keep consistent with the original trained models, this is kept as it is. However,
|
|
# token ids are clipped to a maximum of `n_tokens - 1` to avoid out-of-bounds errors.
|
|
assert len(tokenizer.centers) == (n_numerical_tokens - 1)
|
|
assert len(tokenizer.boundaries) == n_numerical_tokens
|
|
|
|
|
|
@pytest.mark.parametrize("n_tokens", [10, 1000, 10000])
|
|
def test_token_clipping(n_tokens):
|
|
config = ChronosConfig(
|
|
tokenizer_class="MeanScaleUniformBins",
|
|
tokenizer_kwargs={"low_limit": -15, "high_limit": 15},
|
|
n_tokens=n_tokens,
|
|
n_special_tokens=2,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
use_eos_token=True,
|
|
model_type="seq2seq",
|
|
context_length=512,
|
|
prediction_length=64,
|
|
num_samples=20,
|
|
temperature=1.0,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
)
|
|
tokenizer = config.create_tokenizer()
|
|
|
|
huge_value = 1e22 # this large value is assigned to the largest bucket
|
|
token_ids, _, _ = tokenizer._input_transform(context=torch.tensor([[huge_value]]), scale=torch.tensor(([1])))
|
|
assert token_ids[0, 0] == config.n_tokens - 1 # and it's clipped to n_tokens - 1
|